The primary objective of the investment management industry is to maximize returns while minimizing risk. The process of assimilating various investments into a portfolio that accomplishes this objective is one of the primary challenges for the industry. With the rise of sophisticated investment strategies and products, the portfolio construction process only becomes more difficult as managers perform analysis across a wider variety of asset classes, sectors and markets and attempt to quantify increasingly complex relationships. While conceptually sound techniques for optimal portfolio construction have existed for many years, the various assumptions underlying these techniques have not evolved with financial markets. Conventional tools and statistics used in modern portfolio construction suffer from flaws in both assumptions and application. The tools incorrectly assume that a single relational model (e.g., linear, curvilinear) or even multiple relational models can define the complex and dynamic relationships between financial variables. In addition, practitioners using conventional tools often prioritize statistical significance over economic significance. In doing so, practitioners prioritize the “fit” of a model over identifying potential relationships more important to profit and loss. As a result, the financial industry has struggled to construct portfolios with optimum levels of risk and return.